Tissue deformation and shape models in image-guided interventions: a discussion paper

Area of Science:

• Medical Imaging
• Image-Guided Interventions
• Computational Anatomy

Background:

• Rigid body assumptions limit accuracy in image-guided interventions.
• Intraoperative imaging offers real-time spatial data for anatomical structures and devices.
• Soft tissue deformation and inter-individual anatomical variations pose challenges.

Purpose of the Study:

• To promote the concept of active models in image-guided interventions.
• To address limitations of traditional methods by incorporating prior information.
• To demonstrate the utility of deformable models for enhanced intervention planning.

Main Methods:

• Non-rigid registration techniques to determine soft tissue deformation.
• Utilizing free-form deformations and established registration algorithms.
• Employing three types of deformable models: motion, biomechanical, and statistical shape models.

Main Results:

• Active models restrict degrees of freedom in registration algorithms using prior information.
• Preliminary application results demonstrate the feasibility of motion, biomechanical, and statistical shape models.
• Demonstrated ability to update preoperative plans during interventions using intraoperative imaging.

Conclusions:

• Active models offer a significant advancement over rigid body assumptions in image-guided interventions.
• Deformable models, constrained by prior information, provide a robust framework for real-time surgical guidance.
• The presented models show promise for improving the precision and adaptability of image-guided procedures.